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Jinshi Cui, Myongkyoon Yang, Daesik Son, Seongmin Park, and Seong-In Cho

appeared using thermal maps and thermal bruise depth. By multiple regression analysis, the results of analysis of variance showed that impact energy and impact region had a significant effect on temperature, which provided the basis of nondestructive fruit

Open access

Xiaoli Ma, Xuefeng Liu, Pingwei Xiang, Shichun Qiu, Xiangcheng Yuan, and Mei Yang

grades of gummosis were analyzed. In addition, binary logistic regression analysis and ordinal logistics regression analysis through SPSS 18.0 were respectively applied to study the correlation between mineral elements and existence of the gummosis and

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Richard P. Marini, James R. Schupp, Tara Auxt Baugher, and Robert Crassweller

were included in the model as indicator variables, and linear and quadratic terms for FD60 were included as regressor variables, along with all possible interactions. The interaction term with the largest nonsignificant P value was deleted and the

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Xiaotao Ding, Yuping Jiang, Dafeng Hui, Lizhong He, Danfeng Huang, Jizhu Yu, and Qiang Zhou

estimate the cucumber yields, we developed the best regression models of yields with environmental factors of 2014–15. We first tested the relationships between yields and environmental factors to see if linear regression models were adequate. We found that

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Craig E. Kallsen

Experimental design. The premise of this study was that sequential yield records could be regressed against a number of variables to identify Ta thresholds and accumulations that have value in explaining past and predicting future (i.e., subsequent) nut yield

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Tomas N. Hasing, Luis F. Osorio, and Vance M. Whitaker

once in a season and are assessed for stability (usually yield stability) across multiple environments (unique combinations of location and year). However, by modeling correlations for observations arising from the same plant, the regression approach

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Reza Amiri, Kourosh Vahdati, Somayeh Mohsenipoor, Mohammad Reza Mozaffari, and Charles Leslie

system of correlated variables. A path coefficient is a standardized partial regression coefficient and measures the direct influence of a predictor variable on the response (dependent) variable ( Mohammadi et al., 2003 ; Steel and Torrie, 1980 ). This

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Hiroshi Iwanami, Shigeki Moriya, Nobuhiro Kotoda, Sae Takahashi, and Kazuyuki Abe

does not always continue to decrease during storage ( Iwanami et al., 2004 ), and the degree and period of decrease vary widely among cultivars. Johnston et al. (2001 , 2002b) proposed a nonlinear regression model to fit the change in firmness during

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Kurt B. Waldman, David S. Conner, John A. Biernbaum, Michael W. Hamm, and Adam D. Montri

( Conner et al., 2010 ) by using regression analysis and interviews to better understand how management decisions impact profitability. This study differs from the previous study in several key ways. First, this article includes data from three additional

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Bau-Show Lin and Yann-Jou Lin

cooling effect among various shade plants. Then, we used stepwise multiple regression analysis to analyze the contribution of each item of leaf and canopy characteristics and of background microclimate elements to the cooling effect. Materials and Methods